code: def __init__(
      self,
      segment: bool = False,
      embedding_size: int = 100,
      max_position: int = None,
      **kwargs: Any,
  ):
      self.embed_model: tf.keras.Model = None
  
      self.segment: bool = segment  # type: ignore
      self.kwargs = kwargs
  
      self.embedding_size: int = embedding_size  # type: ignore
      self.max_position: int = max_position  # type: ignore
      self.vocab2idx = self.load_embed_vocab()
      self._text_processor: Optional[ABCProcessor] = None
  
file_path: Kashgari/kashgari/embeddings/abc_embedding.py
imports: import json
  from typing import Dict, List, Any, Optional, Union
  import numpy
  import tensorflow
  import tqdm
  import kashgari
  from kashgari.generators import CorpusGenerator
  from kashgari.logger import logger
  from kashgari.processors import ABCProcessor
label_desc: Use image node_id 2 to create a new ABCEmbedding object  with example: obj = ABCEmbedding(segment, embedding_size, max_position)